Why should you care?

OLTP row layouts are great for point lookups but weak for “sum total across 100M orders”. Column-oriented structures shine for warehouse-style queries.

See it live — copy this example

Open mongosh or MongoDB Compass, select database nosqlverse, then run the example. Change one field and run again.

// Analytical-style pipeline that benefits from columnar tech when enabled
db.orders.aggregate([
  { $match: { placedAt: { $gte: ISODate("2026-01-01") } } },
  { $group: { _id: "$status", revenue: { $sum: "$total" }, n: { $sum: 1 } } }
], { allowDiskUse: true })
// In Atlas UI / docs: create a columnstore index on orders for fields status, total, placedAt
// Then re-run and compare explain / latency

Run Example »

Edit the code below and click Run to see the result in Toolliyo’s live editor.

What happened?

• The aggregation scans status and total over a year.
• A column store index can read just those columns more cheaply than touching full documents.
• Exact syntax/support depends on Atlas version — verify current docs when creating the index.

Practice next

1. Identify top analytical aggregations.
2. Check Atlas docs for column store index creation on your tier.
3. Build index on the fields those pipelines use.
4. Add tenantId into the analytical match + index set.
5. Materialize rollups if column indexes are unavailable on your tier.

Remember

Column stores help analytical scans. Pick fields used in heavy $group queries. Still verify support on your Atlas tier.

Year-wide GMV by status

Finance aggregates a year of orders nightly.

Outcome: Columnar acceleration (or rollups) finishes inside the batch window.